Adult influenza vaccination coverage before, during and after the COVID-19 pandemic in Canada
Bibliographic record
Abstract
BACKGROUND: Vaccination prevents seasonal influenza and its complications, particularly among high-risk populations. The COVID-19 pandemic has been reported to impact healthcare behaviors and vaccination patterns. This study aims to assess influenza vaccination coverage and changes in vaccination settings among Canadian adults from the 2018-2019 to the 2023-2024 seasons. METHOD: We conducted a retrospective analysis of data from multiple cycles of the Seasonal Influenza Vaccination Coverage Survey (SIVCS). Vaccination coverage was examined across different seasons, stratified by population groups. Odds ratios (ORs) were calculated to compare vaccination likelihoods across seasons, with 2018-2019 serving as the reference. Chi-square tests were applied to determine whether there were significant differences in the place of vaccination since the pre-pandemic season. RESULTS: When comparing vaccine uptake before, during and after the COVID-19 pandemic, we observed a temporary coverage decline in 2021-2022 season (OR = 0.882, 95% CI = 0.787-0.988) compared to the pre-pandemic season in 2018-2019. By the 2022-2023 and 2023-2024 seasons, vaccination coverage returned to pre-pandemic levels. Coverage among adults aged 18-64 without chronic medical condition consistently remained lower than in other groups. The places of vaccination shifted markedly, with pharmacies becoming the predominant site, increasing from 35.4% in 2018-2019 to 57.4% in 2023-2024, while doctor's offices saw a decline from 32.7 to 15.2% over the same period. CONCLUSION: Our findings highlight the transient effect of the pandemic on flu vaccine uptake in Canada. The increasing use of pharmacies for vaccinations underscores the importance of accessible and convenient vaccination sites. Future efforts should focus on maintaining and improving vaccination coverage through diverse and adaptable vaccination settings.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".